Decision comparison
Semantic Kernel vs LangChain
Semantic Kernel is best for Microsoft-stack enterprise teams building structured AI workflows around Azure, .NET, C#, plugins, and planner patterns. LangChain is best for Python-first teams that need broad integration flexibility, LangGraph-based multi-agent workflows, and LangSmith tracing, evaluation, and deployment. Semantic Kernel provides a $0 MIT-licensed SDK with Azure consumption costs, while LangChain combines its $0 core with LangSmith’s $0/seat Developer tier and $39/seat/month Plus offering.
Semantic Kernel has a named successor from its own vendor
Microsoft merged Semantic Kernel and AutoGen into the Microsoft Agent Framework. Semantic Kernel v1.x remains supported for critical bugs and security issues, but the majority of new features are built for the Agent Framework instead.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
Applies to: Choosing the framework an agent or multi-step LLM application will be built on.
All 2 are agent frameworks.
Quick Comparison
| Decision factor | Semantic Kernel | LangChain |
|---|---|---|
| Multi-Agent Orchestration | Sequential and parallel planner patterns, alongside an Agent Framework and Process Framework for structured, enterprise-oriented AI workflows. | LangGraph with checkpointing and distributed runtime, plus Deep Agents frameworks for multi-agent workflow orchestration and agent swarms. |
| Best For | Microsoft-stack teams needing enterprise-grade AI integration with Azure, .NET, and planner/plugin architecture for well-defined business workflows. | Python-first teams needing maximum flexibility, 300+ integrations, multi-agent workflows, and production observability through LangSmith without Azure lock-in. |
| Pricing | Semantic Kernel is a free, open-source SDK from Microsoft with no licence cost and no paid tier of its own. What it costs to run is whatever the model provider bills for the calls it orchestrates, plus the infrastructure the application runs on. Microsoft directs new projects to the Agent Framework. | $0 / seat (Developer), $39 / seat |
| Primary Languages | C#, Python, Java with first-class .NET support; the GitHub repository’s primary language is C# and latest release is dotnet-1.80.1. | Python, JavaScript/TypeScript; the GitHub repository’s primary language is Python, while LangSmith SDKs support Python, TypeScript, Go, and Java. |
| Product Focus | A framework for building robust, future-proof AI solutions with kernel plugins, memory, agents, processes, observability, security, and filters. | An agent engineering platform combining open-source components with LangSmith observability, evaluation, deployment, tracing, prompt tooling, and human-feedback annotation queues. |
| License & Repository | MIT license; 28,538 GitHub stars. Latest release: dotnet-1.80.1 on 2026-09-03; repository last pushed 2026-09-07. | MIT license; 145,844 GitHub stars. Latest release: langchain-core==1.6.2 on 2026-09-04; repository last pushed 2026-09-07. |
Semantic Kernel
- Multi-Agent Orchestration:
- Sequential and parallel planner patterns, alongside an Agent Framework and Process Framework for structured, enterprise-oriented AI workflows.
- Best For:
- Microsoft-stack teams needing enterprise-grade AI integration with Azure, .NET, and planner/plugin architecture for well-defined business workflows.
- Pricing:
- Semantic Kernel is a free, open-source SDK from Microsoft with no licence cost and no paid tier of its own. What it costs to run is whatever the model provider bills for the calls it orchestrates, plus the infrastructure the application runs on. Microsoft directs new projects to the Agent Framework.
- Primary Languages:
- C#, Python, Java with first-class .NET support; the GitHub repository’s primary language is C# and latest release is dotnet-1.80.1.
- Product Focus:
- A framework for building robust, future-proof AI solutions with kernel plugins, memory, agents, processes, observability, security, and filters.
- License & Repository:
- MIT license; 28,538 GitHub stars. Latest release: dotnet-1.80.1 on 2026-09-03; repository last pushed 2026-09-07.
LangChain
- Multi-Agent Orchestration:
- LangGraph with checkpointing and distributed runtime, plus Deep Agents frameworks for multi-agent workflow orchestration and agent swarms.
- Best For:
- Python-first teams needing maximum flexibility, 300+ integrations, multi-agent workflows, and production observability through LangSmith without Azure lock-in.
- Pricing:
- $0 / seat (Developer), $39 / seat
- Primary Languages:
- Python, JavaScript/TypeScript; the GitHub repository’s primary language is Python, while LangSmith SDKs support Python, TypeScript, Go, and Java.
- Product Focus:
- An agent engineering platform combining open-source components with LangSmith observability, evaluation, deployment, tracing, prompt tooling, and human-feedback annotation queues.
- License & Repository:
- MIT license; 145,844 GitHub stars. Latest release: langchain-core==1.6.2 on 2026-09-04; repository last pushed 2026-09-07.
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Semantic Kernel | LangChain |
|---|---|---|
| GitHub commits, 90d(Product adoption) | 83 | 541 |
| GitHub stars(Product adoption) | 28,000+ | 146,000+ |
| Search interest(Market interest) | 0 | 14 |
| Hacker News mentions, 90d(Community interest) | 1 | 31 |
| PyPI weekly downloads(Product adoption) | 63.1k | 38.2M |
| Stack Overflow questions(Community interest) | 58 | 2.0k |
| npm weekly downloads(Product adoption) | Not available | 2.1M |
As of September 14, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Semantic Kernel
September 14, 2026Package vulnerabilities
PyPI · semantic-kernel@1.44.1
0 vulnerabilities
across 1 package
Repository security score
Not available
LangChain
September 14, 2026Package vulnerabilities
npm · langchain@1.5.11 · PyPI · langchain@1.4.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
LangChain
Feature Comparison
| Feature | Semantic Kernel | LangChain |
|---|---|---|
| Core Capabilities | ||
| Primary Languages | C#, Python, Java with first-class .NET support | Python, JavaScript/TypeScript |
| Architecture Pattern | Plugin + Planner + Memory | Chains + Agents + Retrievers |
| LLM Provider Support | Azure OpenAI, OpenAI, Hugging Face, Ollama | 70+ providers including OpenAI, Anthropic, Google, Cohere |
| Agent Framework | Built-in planner-based agents | LangGraph for stateful multi-agent orchestration |
| RAG Support | Semantic Memory with vector store connectors | Comprehensive RAG pipeline with 50+ document loaders |
| Integrations & Ecosystem | ||
| Vector Store Integrations | Azure AI Search, Pinecone, Qdrant, Weaviate | 40+ vector stores including Pinecone, Chroma, pgvector |
| Plugin/Tool Ecosystem | 30+ built-in plugins | 300+ third-party integrations |
| Community Size | 22,000+ GitHub stars | 142,000+ GitHub stars |
| Streaming Support | Async streaming in C# and Python | Native streaming across all chains and agents |
| Operations & Enterprise | ||
| Observability | OpenTelemetry native with Azure Monitor | LangSmith tracing with annotations and evaluations |
| Deployment Model | Self-hosted with Azure integration | Self-hosted, LangServe, or LangGraph Cloud |
| Enterprise Auth | Azure AD/Entra ID native | SSO via LangSmith Enterprise |
| Evaluation Framework | Basic prompt testing | LangSmith automated scoring with human-in-the-loop |
| Multi-Agent Orchestration | Sequential and parallel planner | LangGraph with checkpointing and distributed runtime |
Core Capabilities
Primary Languages
Architecture Pattern
LLM Provider Support
Agent Framework
RAG Support
Integrations & Ecosystem
Vector Store Integrations
Plugin/Tool Ecosystem
Community Size
Streaming Support
Operations & Enterprise
Observability
Deployment Model
Enterprise Auth
Evaluation Framework
Multi-Agent Orchestration
Which approach fits
Semantic Kernel is best for Microsoft-stack enterprise teams building structured AI workflows around Azure, .NET, C#, plugins, and planner patterns. LangChain is best for Python-first teams that need broad integration flexibility, LangGraph-based multi-agent workflows, and LangSmith tracing, evaluation, and deployment. Semantic Kernel provides a $0 MIT-licensed SDK with Azure consumption costs, while LangChain combines its $0 core with LangSmith’s $0/seat Developer tier and $39/seat/month Plus offering.
When each approach fits
Choose Semantic Kernel if:
Choose Semantic Kernel when your team develops in C# or Java, runs on Azure, and needs enterprise governance through Entra ID and Key Vault. Its structured planner/plugin architecture, Agent Framework, and Process Framework suit well-defined business workflows. The SDK is MIT-licensed and $0, with costs driven by the Azure services you connect.
Choose LangChain if:
Choose LangChain when your team works in Python, needs 300+ integrations, and is building complex multi-agent systems with LangGraph. Use LangSmith for production tracing, evaluations, prompt tooling, and human-feedback annotation queues without Azure lock-in. Start on the $0/seat Developer tier with 5k base traces per month, then use paid usage or $39/seat/month Plus as needed.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Can I use Semantic Kernel and LangChain together in the same project?
Yes, though it is uncommon. Some teams use Semantic Kernel for .NET backend services and LangChain for Python-based data processing pipelines. A more typical pattern is choosing one framework and using REST APIs to integrate with services built on the other.
Which framework has better support for local/self-hosted LLMs?
LangChain has significantly broader support for local model hosting, integrating with Ollama, llama.cpp, vLLM, HuggingFace Transformers, and dozens of other local inference engines. Semantic Kernel supports Ollama and Hugging Face but has fewer options.
How do the two frameworks handle memory and context management?
Semantic Kernel uses a Semantic Memory abstraction that stores embeddings in configurable vector stores. LangChain offers multiple memory types (buffer, summary, entity, vector store) that can be composed and attached to any chain or agent.
Is LangChain's complexity a concern for production deployments?
LangChain's abstraction layers can make debugging difficult, but LangChain Expression Language (LCEL) simplified chain composition, and LangSmith tracing makes production debugging more manageable. Semantic Kernel's simpler architecture has fewer moving parts to debug.